A. Technical Field
The present disclosure relates generally to systems and methods for computer learning that can provide improved computer performance, features, and uses.
B. Background
Question Answering (QA) and Information Extraction systems have proven to be invaluable in wide variety of applications such as medical information collection on drugs and genes, large scale health impact studies, or educational material development.
Recent progress in neural-network-based extractive question answering models are quickly closing the gap with human performance on several benchmark QA tasks such as Stanford Question Answering Dataset (SQuAD), Microsoft MAchine Reading COmprehension Dataset (MS MARCO), or NewsQA, and pave the way towards smarter and more responsive connections between information discovery and its availability in high-stakes decision making.
However, current approaches to extractive question answering face several limitations. First, computation is allocated equally to the entire document, regardless of answer location, with no ability to ignore or focus computation on specific parts, limiting applicability to longer documents. Second, they rely extensively on expensive bidirectional attention mechanisms or must rank all possible answer spans. And third, while data-augmentation for question answering has been proposed, current approaches still do not provide training data that can improve the performance of existing systems nor allow explicit use of the nature of the questions or the entity types to control the generation.
Accordingly, what is needed are systems and methods that address these limitations and provide improved question answering or information extraction.